Style transfer was originally developed for generating a new image [1]} that resembles the texture of a “style” image while maintaining the structure of objects in a “content” image. A well-known example is to convert a photo (content) with the brush touch of Vincent van Gogh (style). The resulting image looks like a painting of the content object drawn with the brush and the technique used in the style image. This style transfer techniques have tried other problem domains such as text and audio. When applied to text data, original texts are transformed between different times [2]}, sentences are revised [3]}, or the sentiments appearing in texts are transferred [4]}. Recently, this technique has been used for transferring styles of a source audio [5]} and generating a new audio that resembles styles or textures of the original style audio data. However, the attempts mostly aimed to generate new audio samples that copy the acoustic texture of style audios and were limited to limited utilities such as varying the timbre of musical instruments. We believe that acoustic style transfer can be effectively used for data augmentation in distributed environments, by transferring acoustic environment style to another environment, and address the data sparsity problem.
This paper provides an experiment that attempts to
find the optimal way for style transfer based acoustic data augmentation.
This paper proposes a pair of evaluation criterion
on the quality of style transfer from the needs of data augmentation.
One is prediction accuracy based, another one is similarity or distance based.
We implement a wide random convolutional neural network as the neural style transfer model,
and carry out a series of experiments on UrbanSound8K. The style transfer
model produces better style transfer than the baseline sound mixing approach.
Meanwhile, the content are well-preserved by our transfer model.
Also, some related hyper-parameters are examined, and both the loss function and
the architecture of the transfer model affect our transfer performance.
